Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories

• URL: http://arxiv.org/abs/2502.01503v2
• Date: Thu, 06 Feb 2025 23:01:55 GMT
• Title: Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories
• Authors: Divyagna Bavikadi, Nathaniel Lee, Paulo Shakarian, Chad Parvis,
• Abstract summary: Bad actors in the maritime industry engage in illegal behaviors after disabling their vessel's automatic identification system (AIS) Machine learning approaches only succeed in identifying the locations of these dark vessels'' in the immediate future. We combine concepts from abduction, logic programming, and rule learning to create an efficient method that approaches full recall of dark vessels.
• Score: 0.6037276428689637
• License:
• Abstract: Bad actors in the maritime industry engage in illegal behaviors after disabling their vessel's automatic identification system (AIS) - which makes finding such vessels difficult for analysts. Machine learning approaches only succeed in identifying the locations of these ``dark vessels'' in the immediate future. This work leverages ideas from the literature on abductive inference applied to locating adversarial agents to solve the problem. Specifically, we combine concepts from abduction, logic programming, and rule learning to create an efficient method that approaches full recall of dark vessels while requiring less search area than machine learning methods. We provide a logic-based paradigm for reasoning about maritime vessels, an abductive inference query method, an automatically extracted rule-based behavior model methodology, and a thorough suite of experiments.

Related papers

• NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration [57.15811390835294]
  This paper describes how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration. We show that this unified policy results in better overall performance when navigating to visually indicated goals in novel environments. Our experiments, conducted on a real-world mobile robot platform, show effective navigation in unseen environments in comparison with five alternative methods.
  arXiv  Detail & Related papers  (2023-10-11T21:07:14Z)
• How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers [94.46825166907831]
  We present a training-free solution to tackle the object goal navigation problem in Embodied AI. Our method builds a structured scene representation based on the classic visual simultaneous localization and mapping (V-SLAM) framework. Our method propagates semantics on the scene graphs based on language priors and scene statistics to introduce semantic knowledge to the geometric frontiers.
  arXiv  Detail & Related papers  (2023-05-26T13:38:33Z)
• Self-Supervised Adversarial Imitation Learning [20.248498544165184]
  Behavioural cloning teaches an agent how to behave via expert demonstrations. Recent approaches use self-supervision of fully-observable unlabelled snapshots of the states to decode state pairs into actions. Previous work uses goal-aware strategies to solve this issue. We address this limitation by incorporating a discriminator into the original framework.
  arXiv  Detail & Related papers  (2023-04-21T12:12:33Z)
• Emergence of Maps in the Memories of Blind Navigation Agents [68.41901534985575]
  Animal navigation research posits that organisms build and maintain internal spatial representations, or maps, of their environment. We ask if machines -- specifically, artificial intelligence (AI) navigation agents -- also build implicit (or'mental') maps. Unlike animal navigation, we can judiciously design the agent's perceptual system and control the learning paradigm to nullify alternative navigation mechanisms.
  arXiv  Detail & Related papers  (2023-01-30T20:09:39Z)
• Occlusion-Aware Crowd Navigation Using People as Sensors [8.635930195821263]
  Occlusions are highly prevalent in such settings due to a limited sensor field of view. Previous work has shown that observed interactive behaviors of human agents can be used to estimate potential obstacles. We propose integrating such social inference techniques into the planning pipeline.
  arXiv  Detail & Related papers  (2022-10-02T15:18:32Z)
• Verifying Learning-Based Robotic Navigation Systems [61.01217374879221]
  We show how modern verification engines can be used for effective model selection. Specifically, we use verification to detect and rule out policies that may demonstrate suboptimal behavior. Our work is the first to demonstrate the use of verification backends for recognizing suboptimal DRL policies in real-world robots.
  arXiv  Detail & Related papers  (2022-05-26T17:56:43Z)
• MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning [65.52675802289775]
  We show that an uncertainty aware classifier can solve challenging reinforcement learning problems. We propose a novel method for computing the normalized maximum likelihood (NML) distribution. We show that the resulting algorithm has a number of intriguing connections to both count-based exploration methods and prior algorithms for learning reward functions.
  arXiv  Detail & Related papers  (2021-07-15T08:19:57Z)
• STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection [10.326887191803275]
  We propose a novel approach to concept drift detection based on a student-teacher learning paradigm. At run-time, our approach is to use the teacher for predicting new instances and monitoring the mimicking loss of the student for concept drift detection. In a set of experiments using 19 data streams, we show that the proposed approach can detect concept drift and present a competitive behaviour.
  arXiv  Detail & Related papers  (2021-03-01T10:51:09Z)
• Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification [81.32981236437395]
  We present a semi-formal verification approach for decision-making tasks based on interval analysis. Our method obtains comparable results over standard benchmarks with respect to formal verifiers. Our approach allows to efficiently evaluate safety properties for decision-making models in practical applications.
  arXiv  Detail & Related papers  (2020-10-19T11:18:06Z)
• COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning [0.0]
  Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. In this article, we explore the potential of Proximal Policy Optimization (PPO), a DRL algorithm with demonstrated state-of-the-art performance on Continuous Control tasks. Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios.
  arXiv  Detail & Related papers  (2020-06-16T22:05:58Z)
• Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning [10.61567813380562]
  We discuss challenges and opportunities in classical machine learning and deep learning for vessel event and anomaly detection. We hope to motivate the research of novel methods and tools, since addressing these challenges is an essential step towards actual intelligent maritime monitoring systems.
  arXiv  Detail & Related papers  (2020-04-07T21:25:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.